To address the problems mentioned above, another big stream is to directly consume sparse point clouds. The pioneer work is PointNet proposed by Qi et al. [1]}. PointNet is able to process unordered point cloud inputs with permutation invariance using a sequence of multi-layer perceptron(MLP). The subsequent work PointNet++[2]} achieves better performance by proposing a hierarchical network that encodes local neighborhood information. Based on PointNet++[2]}, a great number of networks[4]}, [5]}, [6]} with more advanced local feature aggregation techniques are introduced. Apart from MLP-based methods, some recent works propose kernel-based approaches to mimic standard convolution[7]}, [8]}, [9]}, [10]}. In general, point-based approaches suffer from point sampling scalability, neighbor point searching efficiency, point density inconsistency issues. Most recently, Liu et al. [11]} attempt to design a point-voxel CNN that represents 3D data as points to reduce memory footprint, and leverages voxel-based convolution to capture neighborhood features. This network is able to achieve reasonable performance with low memory usage and fast training/inference speed.
